Method and System for Left Ventricle Detection in 2D Magnetic Resonance Images Using Ranking Based Multi-Detector Aggregation

ABSTRACT

A method and system for left ventricle (LV) detection in 2D magnetic resonance imaging (MRI) images is disclosed. In order to detect the LV in a 2D MRI image, a plurality of LV candidates are detected, for example using marginal space learning (MSL) based detection. Candidates for distinctive anatomic landmarks associated with the LV are then detected in the 2D MRI image. In particular, apex candidates and base candidates are detected in the 2D MRI image. One of the LV candidates is selected as a final LV detection result by ranking the LV candidates based on the LV candidates, the apex candidates, and the base candidates using a trained ranking model.

This application claims the benefit of U.S. Provisional Application No.61/120,143, filed Dec. 5, 2008, the disclosure of which is hereinincorporated by reference.

BACKGROUND OF THE INVENTION

The present invention relates to medical imaging of the heart, and moreparticularly, to automatic detection of the left ventricle in 2Dmagnetic resonance images.

Cardiovascular disease is the leading cause of death in developedcountries. Early diagnosis can be effective in reducing the mortality ofcardiovascular disease. Magnetic resonance imaging (MRI) can accuratelydepict cardiac structure, function, perfusion, and myocardial viabilitywith a capacity unmatched by any other imaging modality. Accordingly,MRI is widely accepted as the gold standard for heart chamberquantification, which means that measurements extracted using otherimaging modalities, such as echocardiography and computed tomography(CT), typically must be verified using MRI. Quantification of the leftventricle (LV) is of particular interest among the four heart chambersbecause it pumps oxygenated blood from the heart to the rest of thebody. In order to quantify functional measurements of the LV, it isnecessary to detect or segment the LV in an MRI image.

Automatic LV detection in MRI images is a challenging problem due tolarge variations in orientation, size, shape, and image intensity of theLV. First, unlike CT, MRI is flexible in selecting the orientation ofthe imaging plane, and this helps cardiologists to capture the best viewfor diagnosis. However, this flexibility presents a large challenge forautomatic LV detection because both the position and orientation of theLV are unconstrained in an image. The LV is a roughly rotation symmetricobject around its long axis, which is generally defined as the axisconnecting the LV apex to the center of the mitral valve. Long-axisviews (where the imaging plane passes through the LV long axis) areoften captured to perform LV measurement. However, the orientation ofthe LV long axis in the image is unconstrained. Second, an MRI imageonly captures a 2D intersection of a 3D object, therefore information islost compared to a 3D volume. The image plane can be rotated to getseveral standard cardiac views, such as the apical-two-chamber (A2C)view, the apical-three-chamber (A3C), the apical-four-chamber (A4C), andthe apical-five-chamber (A5C) view. However, this view information isnot available to help automatic LV detection. Although the LV and rightventricle (RV) have quite different 3D shapes, in the 2D A4C view, theLV is likely to be confused with the RV. Third, the LV shape changessignificantly in a cardiac cycle. The heart is a non-rigid shape, whichchanges shape as it beats to pump blood to the body. In order to studythe dynamics of the heart, a cardiologist needs to capture images fromdifferent cardiac phases. The LV shape changes significantly from theend-diastolic (ED) phase (when the LV is the largest) to theend-systolic (ES) phase (when the LV is the smallest). Finally, MRIimages captured with different scanners or different imaging protocolshave large variations in intensity. Accordingly, an automatic LVdetection method which overcomes the above challenges is desirable.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a method and system for automatic leftventricle (LV) detection in 2D MRI images. Embodiments of the presentinvention separately detect LV candidates, using marginal space learning(MSL), and anatomic landmark candidates related to the LV. A learnedranking model is then used to aggregate features extracted from the LVcandidates and anatomic landmark candidates to rank the LV candidates inorder to select the best LV candidate.

In one embodiment of the present invention, a plurality of LV candidatesare detected, for example using MSL. Apex candidates and base candidatesare then detected in the 2D MRI image. The LV candidates are rankedbased on geometrical relationships between each LV candidate and theother LV candidates, the apex candidates, and the base candidates usinga trained ranking model. The top-ranked LV candidate is selected as afinal LV detection result.

These and other advantages of the invention will be apparent to those ofordinary skill in the art by reference to the following detaileddescription and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates object localization using MSL according to anembodiment of the present invention;

FIG. 2 illustrates a method for LV detection in a 2D MRI image accordingto an embodiment of the present invention;

FIG. 3 illustrates LV and anatomical landmark detection results inexemplary 2D MRI images;

FIG. 4 illustrates a RankBoost algorithm for training a ranking model;

FIG. 5 illustrates exemplary LV detection results; and

FIG. 6 is a high level block diagram of a computer capable ofimplementing the present invention.

DETAILED DESCRIPTION

The present invention is directed to a method and system for automaticleft ventricle (LV) detection in 2D magnetic resonance imaging (MRI)images. Embodiments of the present invention are described herein togive a visual understanding of the left ventricle detection method. Adigital image is often composed of digital representations of one ormore objects (or shapes). The digital representation of an object isoften described herein in terms of identifying and manipulating theobjects. Such manipulations are virtual manipulations accomplished inthe memory or other circuitry/hardware of a computer system.Accordingly, it is to be understood that embodiments of the presentinvention may be performed within a computer system using data storedwithin the computer system.

Discriminative learning based approaches are efficient and robust forsolving many 2D detection problems. In such methods, shape detection andlocalization is formulated as a classification problem: whether an imageblock contains the target shape or not. In order to build a robustsystem, a classifier only tolerates limited variation in object pose.The object is found by scanning the classifier exhaustively over allpossible combination of locations, orientations, and scales. This searchstrategy is different from other parameter estimation approaches, suchas deformable models, where an initial estimate is adjusted (e.g., usinga gradient descent technique) to optimize a predefined objectivefunction. Exhaustive searching makes the system robust under localminima. However, it is challenging to extend such learning basedtechniques using exhaustive searching to a high dimensional spacebecause the number of hypotheses increases exponentially with respect tothe dimensionality of the parameter space. Recently, marginal spacelearning (MSL) has been developed to apply learning based techniques for3D object detection. For example, a method for MSL-based heart chambersegmentation is described in detail in U.S. Patent ApplicationPublication No. 2008/0101676, entitled “System and Method for SegmentingChambers of a Heart in a Three Dimensional Image”, which is incorporatedherein by reference. In order to efficiently localize an object usingMSL, parameter estimation is performed in a series of marginal spaceswith increasing dimensionality. FIG. 1 illustrates object localizationusing MSL according to an embodiment of the present invention. Asillustrated in FIG. 1, object localization or detection in an inputimage is split into three steps: object position estimation (step 102),position-orientation estimation (step 104), andposition-orientation-scale estimation (step 106). After each step, a fewcandidates are obtained for the following estimation step. Thecandidates resulting from the position-orientation-scale estimation step(step 106) are then aggregated at step 108 to generate a detectionresult. MSL has been successfully applied to many 3D object detectionproblems in medical imaging.

MSL was originally proposed for 3D object detection. Although MSL can beapplied to 2D object detection to detect the LV in a 2D MRI image, thisdetection problem is challenging due to large variations in orientation,size, shape, and image intensity of the LV. The performance of a singlewhole-object detector is limited. Accordingly, in addition to the LVwhole-object detected using MSL, embodiments of the present inventionalso detect several LV landmarks, such as the LV apex and two annuluspoints, and aggregate the detected candidates from the whole-objectdetector and landmark detectors in order to improve the robustness ofthe LV detection. Further, embodiments of the present invention utilizea ranking-based method to aggregate information from the detected LVcandidates and detected landmark candidates. In this ranking-basedmethod a ranking model is trained to sort the LV whole-body candidatesaccording to the amount of support they get from all of the detectors inorder to achieve a more robust LV detection result and reduce the effectof detection outliers.

FIG. 2 illustrates a method for LV detection in a 2D MRI image accordingto an embodiment of the present invention. The method of FIG. 1transforms MRI image data representing a patient's heart to detect orlocalize the location of the left ventricle in the patient's heart. Atstep 202, a 2D MRI image is received. The MRI image can be received froman MRI scanning device, or can be a previously stored MRI image loadedfrom memory or storage of a computer system, or some other computerreadable medium.

At step 204, LV candidates are detected in the 2D MRI image using MSL.To localize a 2D object, such as the LV in a 2D MRI image, fiveparameters must be estimated: two for position, one for orientation, andtwo for anisotropic scaling. These parameters can be visuallyrepresented as a bounding box of the LV, which tightly encloses the LV.The box is aligned with the direction connecting the LV apex and base.The length of the box on this direction is the distance between the apexand the base center (which is defined as the center of two annuluspoints). The length along the other direction is set to tightly enclosethe whole LV. The box center is defined as the center of the LV boundingbox. It is close to the center of line connecting the apex and basesince the LV has a roughly rotation symmetric shape around its longaxis.

In order to detect LV candidates using MSL, a detector is trained foreach MSL step (see FIG. 1) based on annotated training data. In thefirst stage of MSL, the position of the object (LV) is estimated in theimage using a trained position detector. For the position estimation,orientation and scales are treated as intra-class variations, thereforelearning is constrained in a marginal space with two dimensions.According to an advantageous implementation, the position detector canbe trained based on the training data using Haar wavelet features. Givena set of candidate pixels in the training data, the candidates are splitinto two groups, positive and negative, based on their distance to theground truth. For example, in an advantageous implementation, a positivesample (X,Y) should satisfy:

max{|X−X _(t) |,|Y−Y _(t)|}≦2 mm,   (1)

and a negative sample should satisfy:

max{|X−X _(t) |,|Y−Y _(t)|}>6 mm.   (2)

Here, (X_(t),Y_(t)) is the ground truth of the object (LV) center. Thesearching step for position estimation can be 1 pixel. All positivesamples in the training data satisfying Equation (1) are collected fortraining. Since the total number of negative samples from a training setis typically very large, a limited number of negatives are used fortraining. For example, approximately three million negatives can berandomly sampled from the whole training set.

Given a set of positive and negative training samples, 2D Haar waveletfeatures can be extracted from the training images for the samples. Aclassifier (detector) is then trained based on these features using aprobabilistic boosting tree (PBT). The PBT boosts the performance ofweak classifiers to generate a strong tree-structure classifier. Thetrained position detector is used to scan a training image a preserve asmall number of top LV position candidates. The number of preservedcandidates can be tuned based on the performance of the trainedclassifier and the target detection speed of the system. According to anadvantageous implementation, 1000 candidates can be preserved in orderto ensure that most training images have at least one true positiveamong the candidates.

After the position detector is trained, the position-orientationdetector is then trained. Suppose for a given training image, 1000candidates, (X_(i), Y_(i)), i=1, . . . ,1000 are preserved for the LVposition. A detector is then trained to estimate both the position andorientation. The parameter space for this stage is three dimensional (2Dfor position and 1D for orientation), so the dimension of the candidatesmust be augmented. For each position candidate, the orientation space issampled uniformly to generate hypotheses for position-orientationestimation. The orientation search step can be set to be five degrees,corresponding to 72 hypotheses for the orientation subspace for eachposition candidate. Among all of these hypotheses some are close to theground truth (positive) and some are far away (negative). The learninggoal is to distinguish the positive and negative samples using imagefeatures. In an advantageous implementation, a hypothesis (X, Y, θ) isregarded as a positive sample if it satisfies both Equation (1) and:

|θ−θ_(t)|≦5 degrees,   (3)

and a negative sample satisfies either Equation (2) or:

|θ−θ_(t)|>10 degrees,   (4)

where θ_(t) represents the ground truth of the LV orientation. Similarlyto training the position detector, a number of negative samples (e.g.,three million) can be randomly sampled over the training set.

Since aligning Haar wavelet features to a specific orientation is notefficient, steerable features can be used for training theposition-orientation detector in order to avoid image rotation. A PBT isused to train a classifier (detector) based on the steerable features.The trained position-orientation detector is used to prune thehypotheses to preserve only a few candidates (e.g., 100) for objectposition and orientation.

Once the position-orientation detector is trained, theposition-orientation-scale detector is trained to estimate the fullparameter of the LV box. The training of the detector for full parameterestimation is analogous to training the position-orientation detector,except learning is performed in the full five-dimensional similaritytransformation space. The dimension of each position-orientationcandidate is augmented by scanning the scale subspace uniformly andexhaustively. For example, in an advantageous implementation, the rangesof S_(x) and S_(y) of the LV can be [62.9, 186.5] mm and [24.0, 137.8]mm, respectively, and the search step for the scales can be set to 6 mm.In this case, to cover the whole range, 22 uniformly distributed samplesare generated for S_(x) and 20 are generated for S_(y). In total, thereare 440 hypotheses for the scale subspace for each position-orientationcandidate.

In an advantageous implementation, a hypothesis (X, Y, θ, S_(x), S_(y))is regarded as positive if it satisfies, Equations (1), (3), and:

max{|S _(x) −S _(x) ^(t) |, S _(y) −S _(y) ^(t)|}≧6 mm,   (5)

and a negative sample satisfies any one condition of Equations (2), (4),or:

max{|S _(x) −S _(x) ^(t) |,|S _(y) −S _(y) ^(t)|}>12 mm,   (6)

where S_(x) ^(t) and S_(y) ^(t) represent the ground truth of the LVscales. A number of negative samples (e.g., three million) can berandomly sampled over the training set, and a PBT-based classifier canbe trained using steerable features.

In order to detect the LV candidates in the received 2D MRI image instep 204, the image is first normalized. For example, the image can benormalized to a 1 mm resolution. All of the pixels of the normalizedimage are then tested using the trained position detector in order todetect the pixels with the highest probability of being the center ofthe LV. A predetermined number of position candidates detected by theposition detector with the highest probability are kept. For example,the top 1000 position candidates, (X_(i), Y_(i)), i=1, . . . ,1000, canbe kept. Each position candidate is augmented with a plurality oforientations to generate position-orientation hypotheses. For example,each position candidate can be augmented with 72 orientation hypotheses(X_(i), Y_(i), θ_(j)), j=1, . . . ,72 to generate 1000×72=72,000position-orientation hypotheses. The position-orientation hypotheses aretested using the trained position-orientation detector to detect the topposition-orientation candidates. For example, the top 100position-orientation candidates detected by the position-orientationdetector can be retained, ({circumflex over (X)}_(i), Ŷ_(i), {circumflexover (θ)}_(i)), i=1, . . . ,100. Each position-orientation candidate isaugmented with a plurality of scales to generateposition-orientation-scale hypotheses. For example, eachposition-orientation candidate can be augmented with 440 scalehypotheses to generate 100×440=44,000 position-orientation-scalehypotheses. The position-orientation-scale hypotheses are then testedusing the trained position-orientation-scale detector to detect the topLV box candidates. This results in a predetermined number of LVcandidates. For example, the top 100 LV candidates detected by theposition-orientation-scale detector with the highest probability can beretained.

FIG. 3 illustrates LV and anatomical landmark detection results inexemplary 2D MRI images. As illustrated in FIG. 3, image (a) shows LVcandidates 302 detected using MSL in an A4C canonical view 2D MRI image.The LV and RV are similar in both shape and appearance in this view.Previous MSL applications for 3D object detection aggregate the topcandidates resulting from the position-orientation-scale detector usingclustering analysis to generate the final detection result. Image (e) ofFIG. 3 shows the LV detection result 308 resulting from aggregating thecandidates 302 shown in image (a). Since more of the candidates 302 aredistributed around the RV, the wrong object is selected as the finalaggregated detection result 308. Thus, according to an embodiment of thepresent invention, the LV candidates resulting from theposition-orientation-scale detector are aggregated with otherdistinctive anatomic landmarks associated with the LV in order achievemore robust LV detection results.

Returning to FIG. 2, at step 206, apex candidates are detected in thereceived 2D MRI image. For example, similar to the detection of thewhole LV candidates described above, MSL can be used to detectcandidates for the LV apex, which is a well-known anatomical landmark.Each apex candidate can be visually represented as a box surrounding theLV apex. Although the apex is just a point, it is detected as a regionby defining an oriented box around the apex. In this way, theorientation and size information of the surrounding region can beexploited to distinguish the apex from other confusing points. Accordingto an advantageous implementation, the top 100 apex candidates resultingfrom the apex detection can be retained. Image (b) of FIG. 3 shows apexcandidates 304 detected in an exemplary 2D MRI image. Image (f) of FIG.3 shows the final detection result 310 of the apex generated byaggregating the apex candidates 304 of image (b).

Returning to FIG. 2, at step 208, base candidates are detected in thereceived 2D MRI image. Each base candidate can be visually representedas a box centered at the basal center (the mitral valve center) andtightly enclosing the annulus points of the mitral valve. The basecandidates can be detected using MSL, similar to the detection of thewhole LV candidates and the apex candidates described above. Accordingto an advantageous embodiment of the present invention, the top 100 basecandidates resulting from the base detection can be retained. In thisstep (as in the apex detection and the LV detection), selection of thetop candidates is based on the detection score. The PBT classifier willassign a high score to a good candidate (closing to the true position)and a low score to a bad candidate (far away from the true position).Image (c) of FIG. 3 shows base candidates 206 detected in an exemplary2D MRI image. Image (g) shows the final detection result 212 of the basebox generated by aggregating the base candidates 206 of image (c).

Returning to FIG. 2, at step 210, the best LV candidate is selected byranking the LV candidates based on geometric relationships between eachLV candidate and the other LV candidates, the apex candidates, and thebase candidates using a trained ranking model. Since the traineddetectors (LV, apex, and base) tend to detect multiple candidates aroundthe true position, while candidates detected at false positions aresporadic. Accordingly, a correct LV box should have many surroundingdetected LV candidates. Furthermore, around the apex of a correct LVbox, there should be many detected apex candidates, and around the baseof a correct LV box, there should be many detected base candidates.Based on the geometric relationships between LV candidates, apexcandidates, and base candidates in training data, a ranking model istrained and used to select the best LV box among the LV candidates.

All ranking features are based on the geometric relationship between aparticular LV candidate box and the other candidate boxes. Given boxesA(X^(A), Y^(A), θ^(A), S_(x) ^(A), S_(y) ^(A)) and B(X^(B), Y^(B),θ^(B), S_(x) ^(B), S_(y) ^(B)), the following four geometricrelationships can be calculated: 1) The center to center distance, whichis defined as D_(C)(A, B)=√{square root over((X^(A)−X^(B))²+(Y^(A)−Y^(B))²)}{square root over((X^(A)−X^(B))²+(Y^(A)−Y^(B))²)}; 2) The orientation distance, whichisdefined as D_(O)(A, B)=∥θ^(A)−θ^(B)∥; 3) The overlapping ratio, which isdefined as the intersection area of A and B divided by their union area,O(A, B)=(A ∩ B)/(A ∪ B); and 4) The vertex distance, D_(v)(A, B). Eachbox has four vertices V₁, V₂, V₃, V₄, and these vertices can beconsistently assigned with an order based on the box orientation. Thevertex-vertex distance is defines as the mean Euclidean distance betweenthe corresponding vertices:

$\begin{matrix}{{D_{V}\left( {A,B} \right)} = {\frac{1}{4}{\sum\limits_{i = 1}^{4}{{{V_{i}^{A} - V_{i}^{B}}}.}}}} & (7)\end{matrix}$

Among all of the above described features, the center-center andorientation distances only partially measure the difference between twoboxes. The overlapping ration has ambiguity with respect to theorientation. For example, rotating box A around its center by 180degrees does not change the overlapping ratio to any other boxes. Thevertex-vertex distance is a most comprehensive distance measure, asD_(v)(A, B)=0 if and only if boxes A and B are the same.

Given an LV candidate box A, three groups of features are extracted andused to learn the ranking model. The first group of features areextracted from the other LV candidate boxes. First all other LVcandidate boxes are sorted using the vertex-vertex distance to box A.Therefore, a consistent ordering can be assigned to the extractedfeature set, across different boxes. Supposing that box B is another LVcandidate box, five features can be extracted from box B, including thedetected score (assigned by the LV detector) and the above describedfour geometric features between boxes A and B. For the example in which100 LV candidate boxes are detected, a total of 99×5=495 features areselected in this group of features for each LV candidate box.

The second group of features is based on the geometric relationship ofthe LV candidate box A to all of the detected apex candidates. From boxA, the position of its apex, C_(p) ^(A), can be predicted. Inparticular, C_(p) ^(A) is assigned as the center of the box edge of theapex side. Given a detected apex candidate box C, the following threefeatures are extracted: 1) the detection score of box C (assigned by theapex detector); 2) distance to the predicted apex position of the LVcandidate box; and 3) orientation distance, D_(o)(A, C). For theexample, in which 100 apex candidates are detected, a total of 100×3=300features are detected for each LV candidate box based on the geometricrelationship between the LV candidate box and the 100 apex candidates.

The third group of features is based on the geometric relationship ofthe LV candidate box A to all of the detected base candidates. From boxA, the position of its base can be predicted. Given a detected basecandidate box D, the following three features are extracted: 1) thedetection score of box D (assigned by the base detector); 2) distance tothe predicted base position of the LV candidate box; and 3) orientationdistance, D_(o)(A, D). For the example in which 100 base candidates aredetected, a total of 100×3=300 features are detected for each LVcandidate box based on the geometric relationship between the LVcandidate box and the 100 base candidates. Including the score ofdetection score of the LV candidate box A itself and the three groups offeatures detected for each LV candidate, a total of 1+495+300+300=1096features can be extracted to train the ranking model.

In order to train the ranking model based on the extracted features, theRankBoost learning algorithm can be used to train (learn) a rankingmodel that selects the best LV candidate from the candidate list. Thegoal of the RankBoost learning is to minimize the (weighted) number ofpairs of boxes that are mis-ordered by the final ranking, relative tothe given ground truth in the annotated training data. Suppose thelearner is provided with ground truth about the relative ranking of anindividual pair of boxes x₀ and x₁. Suppose that box x₁ should be rankedabove box x₀ , otherwise a penalty D(x_(o), x₁) is imposed. According toa possible implementation an equally weighted penalty D(x_(o), x₁) maybe used. It is also possible that the penalty weights D(x_(o), x₁) canbe normalized to a probability distribution. The learning goal is tosearch for the final ranking function H that minimizes the ranking loss:

$\begin{matrix}{{{rloss}_{D}(H)} = {\sum\limits_{{x\; 0},{x\; 1}}{{D\left( {x_{o},x_{1}} \right)}{{\delta \left\lbrack {{H\left( x_{1} \right)} \leq {H\left( x_{0} \right)}} \right\rbrack}.}}}} & (8)\end{matrix}$

Here, δ[.] is 1 if the predicate holds and 0 otherwise.

FIG. 4 illustrates the RankBoost algorithm for training the rankingmodel. As shown in FIG. 4, h_(t) is a weak ranking function, whichcorresponds to each individual feature described above. The finallearned ranking model H is an optimal linear combination of best Tfeatures:

$\begin{matrix}{{{H(x)} = {\sum\limits_{t = 1}^{T}{\alpha_{t}{h_{t}(x)}}}},} & (9)\end{matrix}$

where x is an LV candidate, h_(t) corresponds to an individual feature,T denotes the number of features used, and α_(t) is a weight of featureh_(t) . According to a possible implementation, T can be set equal to 25to rank the LV candidates based on the 25 best features. The optimalweight α_(t) for each feature h_(t) can be found numerically using thewell-known Newton-Raphson method.

In order to select the best LV candidate of an input 2D MRI image, theabove described features are extracted for each detected LV candidate,and the LV candidates are ranked based on the extracted features usingthe ranking model trained as described above. The ranking modelcalculates a ranking score for each LV candidate, and the LV candidatewith the highest ranking score is selected as the final LV detectionresult.

Image (d) of FIG. 3 shows the LV candidates 302, apex candidates 304,and base candidates 306 detected for an exemplary 2D MRI image. Image(h) shows the final detection result for the LV 314 generated by rankingthe LV candidates 302 based on geometric relationships between the LVcandidates 302, the apex candidates 304 and the base candidates 306using the trained ranking model.

As a byproduct of the component-based detection of the LV, detectionresults for the apex and the base of the detected LV can be generated aswell. For example, after the final LV detection result is determined,the predicted positions of the apex and the base can be determined forthe final LV detection result, and the trained apex and base detectorscan be run in a constrained area around these predicted regions togenerate apex and base detection results that are consistent with thedetected LV. Image (h) of FIG. 3 illustrates final detection results forthe apex 316 and the base 318.

Returning to FIG. 2, at step 212, the LV detection results are output.In addition to the detected LV, the LV detection results that are outputcan also include the detected apex and the detected base as well. Forexample, the LV detection results can be output by displaying the LVdetection results on a display of a computer system, or other displaydevice. It is also possible that the LV detection results can be outputby storing the detected LV and corresponding anatomic features, forexample, on a storage or memory of a computer system or on a computerreadable medium. The output LV detection results can also be used foradditional processing of the 2D MRI image. For example, the detected LVcan be used in a method for LV quantification to measure activity of theLV.

FIG. 5 illustrates exemplary LV detection results detected using themethod of FIG. 2. As illustrated in FIG. 5 each of images 500, 510, 520,530, 540, and 550 shows a detected LV 502, 512, 522, 532, 542, and 552,a detected apex 504, 514, 524, 534, 544, and 554, and detected mitralvalve annulus points 506, 516, 526, 536, 546, 556, respectively. Themitral valve annulus points 506, 516, 526, 536, 546, 556 are anatomiclandmarks that are generated from the detected base box. It isstraightforward to determine the annulus points from a base box sincethe box is defined in the following way. The center of the box isdefined as the center of two annulus points. The box is aligned with thedirection connecting two annulus points. The length of box in thedirection connection the two annulus points is the distance between theannulus points. Therefore, each annulus point lies at the center of oneof the box's sides.

The above-described methods for LV detection in an input 2D MRI imagemay be implemented on a computer using well-known computer processors,memory units, storage devices, computer software, and other components.A high level block diagram of such a computer is illustrated in FIG. 6.Computer 602 contains a processor 604 which controls the overalloperation of the computer 602 by executing computer program instructionswhich define such operation. The computer program instructions may bestored in a storage device 612, or other computer readable medium (e.g.,magnetic disk, CD ROM, etc.) and loaded into memory 610 when executionof the computer program instructions is desired. Thus, the steps of themethod of FIGS. 2 and 4 may be defined by the computer programinstructions stored in the memory 610 and/or storage 612 and controlledby the processor 604 executing the computer program instructions. An MRscanning device 620 can be connected to the computer 602 to input MRIimages to the computer 602. It is possible to implement the MR scanningdevice 620 and the computer 602 as one device. It is also possible thatthe MR scanning device 620 and the computer 602 communicate wirelesslythrough a network. The computer 602 also includes one or more networkinterfaces 606 for communicating with other devices via a network. Thecomputer 602 also includes other input/output devices 608 that enableuser interaction with the computer 602 (e.g., display, keyboard, mouse,speakers, buttons, etc.). One skilled in the art will recognize that animplementation of an actual computer could contain other components aswell, and that FIG. 6 is a high level representation of some of thecomponents of such a computer for illustrative purposes.

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the invention disclosed herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present invention and that variousmodifications may be implemented by those skilled in the art withoutdeparting from the scope and spirit of the invention. Those skilled inthe art could implement various other feature combinations withoutdeparting from the scope and spirit of the invention.

1. A method for left ventricle (LV) detection in a 2D magnetic resonanceimaging (MRI) image, comprising: detecting a plurality of LV candidatesin the 2D MRI image; detecting a plurality of apex candidates in the 2DMRI image; detecting a plurality of base candidates in the 2D MRI image;and selecting one of the plurality LV candidates by ranking theplurality of LV candidates based on geometrical relationships betweeneach detected LV candidate and the other detected LV candidates, betweeneach detected LV candidate and the detected apex candidates, and betweeneach detected LV candidate and the detected base candidates using atrained ranking model.
 2. The method of claim 1, wherein said step ofdetecting a plurality of LV candidates in the 2D MRI image comprises:detecting the plurality of LV candidates using marginal space learning(MSL).
 3. The method of claim 1, wherein said step of detecting aplurality of LV candidates in the 2D MRI image comprises: detecting aplurality of position candidates for the LV in the 2D MRI image using atrained position detector; generating a plurality ofposition-orientation hypotheses from each of the plurality of positioncandidates; detecting a plurality of position-orientation candidatesfrom the plurality of position-orientation hypotheses using a trainedposition-orientation detector; generating a plurality ofposition-orientation-scale hypotheses from each of the plurality ofposition-orientation candidates; and detecting the plurality of LVcandidates from the plurality of position-orientation-scale hypothesesusing a trained position-orientation-scale detector.
 4. The method ofclaim 3, wherein each of the position detector, the position-orientationdetector, and the position-orientation-scale detector are trained usinga probabilistic boosting tree (PBT).
 5. The method of claim 2, whereinthe plurality of apex candidates and the plurality of base candidatesare detected using MSL.
 6. The method of claim 1, wherein said step ofselecting one of the plurality LV candidates by ranking the plurality ofLV candidates based on geometrical relationships between each detectedLV candidate and the other detected LV candidates, between each detectedLV candidate and the detected apex candidates, and between each detectedLV candidate and the detected base candidates using a trained rankingmodel comprises: extracting a plurality of features for each detected LVcandidate, said features including a first group of features based ongeometrical relationships between each detected LV candidate and theother detected LV candidates, a second group of features based ongeometrical relationships between each detected LV candidate and thedetected apex candidates, and a third group of features based ongeometrical relationships between each detected LV candidate and thedetected base candidates; and ranking the plurality of LV candidatesbased on the extracted features using the trained ranking model.
 7. Themethod of claim 6, wherein the first group of features includes adetection score for each of the detected LV candidates, a center-centerdistance between each detected LV candidate and each of the otherdetected LV candidates, an orientation distance between each detected LVcandidate and each of the other detected LV candidates, an overlappingarea between each detected LV candidate and each of the other detectedLV candidates, and a vertex-vertex distance between each detected LVcandidate and each of the other detected LV candidates.
 8. The method ofclaim 6, wherein the second group of features includes a detection scorefor each of the detected apex candidates, a distance between a predictedposition of an apex in each of the detected LV candidates and each ofthe detected apex candidates, and an orientation distance between eachof the detected LV candidates and each of the detected apex candidates.9. The method of claim 6, wherein the third group of features includes adetection score for each of the detected base candidates, a distancebetween a predicted position of a base in each of the detected LVcandidates and each of the detected base candidates, and an orientationdistance between each of the detected LV candidates and each of thedetected base candidates.
 10. The method of claim 1, wherein the trainedranking model is trained based on geometrical relationships between LVcandidates, apex candidates, and base candidates detected in annotatedtraining data.
 11. The method of 1, further comprising: determiningpredicted apex and base positions in the 2D MRI image based on theselected one of the plurality of LV candidates; detecting the apex ofthe LV around the predicted apex position in the 2D MRI image; anddetecting the base of the LV around the predicted base position in the2D MRI image.
 12. An apparatus for left ventricle (LV) detection in a 2Dmagnetic resonance imaging (MRI) image, comprising: means for detectinga plurality of LV candidates in the 2D MRI image; means for detecting aplurality of apex candidates in the 2D MRI image; means for detecting aplurality of base candidates in the 2D MRI image; and means forselecting one of the plurality LV candidates by ranking the plurality ofLV candidates based on geometrical relationships between each detectedLV candidate and the other detected LV candidates, between each detectedLV candidate and the detected apex candidates, and between each detectedLV candidate and the detected base candidates using a trained rankingmodel.
 13. The apparatus of claim 12, wherein said means for detecting aplurality of LV candidates in the 2D MRI image comprises: means fordetecting a plurality of position candidates for the LV in the 2D MRIimage using a trained position detector; means for generating aplurality of position-orientation hypotheses from each of the pluralityof position candidates; means for detecting a plurality ofposition-orientation candidates from the plurality ofposition-orientation hypotheses using a trained position-orientationdetector; means for generating a plurality of position-orientation-scalehypotheses from each of the plurality of position-orientationcandidates; and means for detecting the plurality of LV candidates fromthe plurality of position-orientation-scale hypotheses using a trainedposition-orientation-scale detector.
 14. The apparatus of claim 12,wherein the plurality of LV candidates, the plurality of apexcandidates, and the plurality of base candidates are detected using MSL.15. The apparatus of claim 12, wherein said means for selecting one ofthe plurality LV candidates by ranking the plurality of LV candidatesbased on geometrical relationships between each detected LV candidateand the other detected LV candidates, between each detected LV candidateand the detected apex candidates, and between each detected LV candidateand the detected base candidates using a trained ranking modelcomprises: means for extracting a plurality of features for eachdetected LV candidate, said features including a first group of featuresbased on geometrical relationships between each detected LV candidateand the other detected LV candidates, a second group of features basedon geometrical relationships between each detected LV candidate and thedetected apex candidates, and a third group of features based ongeometrical relationships between each detected LV candidate and thedetected base candidates; and means for ranking the plurality of LVcandidates based on the extracted features using the trained rankingmodel.
 16. The apparatus of claim 12, wherein the trained ranking modelis trained based on geometrical relationships between LV candidates,apex candidates, and base candidates detected in annotated trainingdata.
 17. A computer readable medium encoded with computer executableinstructions for left ventricle (LV) detection in a 2D magneticresonance imaging (MRI) image, the computer executable instructionsdefining steps comprising: detecting a plurality of LV candidates in the2D MRI image; detecting a plurality of apex candidates in the 2D MRIimage; detecting a plurality of base candidates in the 2D MRI image; andselecting one of the plurality LV candidates by ranking the plurality ofLV candidates based on geometrical relationships between each detectedLV candidate and the other detected LV candidates, between each detectedLV candidate and the detected apex candidates, and between each detectedLV candidate and the detected base candidates using a trained rankingmodel.
 18. The computer readable medium of claim 17, wherein thecomputer executable instructions defining the step of detecting aplurality of LV candidates in the 2D MRI image comprise computerexecutable instructions defining the steps of: detecting a plurality ofposition candidates for the LV in the 2D MRI image using a trainedposition detector; generating a plurality of position-orientationhypotheses from each of the plurality of position candidates; detectinga plurality of position-orientation candidates from the plurality ofposition-orientation hypotheses using a trained position-orientationdetector; generating a plurality of position-orientation-scalehypotheses from each of the plurality of position-orientationcandidates; and detecting the plurality of LV candidates from theplurality of position-orientation-scale hypotheses using a trainedposition-orientation-scale detector.
 19. The computer readable medium ofclaim 17, wherein the plurality of LV candidates, the plurality of apexcandidates, and the plurality of base candidates are detected using MSL.20. The computer readable medium of claim 18, wherein the computerexecutable instructions defining the step of selecting one of theplurality LV candidates by ranking the plurality of LV candidates basedon geometrical relationships between each detected LV candidate and theother detected LV candidates, between each detected LV candidate and thedetected apex candidates, and between each detected LV candidate and thedetected base candidates using a trained ranking model comprise computerexecutable instructions defining the steps of: extracting a plurality offeatures for each detected LV candidate, said features including a firstgroup of features based on geometrical relationships between eachdetected LV candidate and the other detected LV candidates, a secondgroup of features based on geometrical relationships between eachdetected LV candidate and the detected apex candidates, and a thirdgroup of features based on geometrical relationships between eachdetected LV candidate and the detected base candidates; and ranking theplurality of LV candidates based on the extracted features using thetrained ranking model.
 21. The computer readable medium of claim 20,wherein the first group of features includes a detection score for eachof the detected LV candidates, a center-center distance between eachdetected LV candidate and each of the other detected LV candidates, anorientation distance between each detected LV candidate and each of theother detected LV candidates, an overlapping area between each detectedLV candidate and each of the other detected LV candidates, and avertex-vertex distance between each detected LV candidate and each ofthe other detected LV candidates.
 22. The computer readable medium ofclaim 20, wherein the second group of features includes a detectionscore for each of the detected apex candidates, a distance between apredicted position of an apex in each of the detected LV candidates andeach of the detected apex candidates, and an orientation distancebetween each of the detected LV candidates and each of the detected apexcandidates.
 23. The computer readable medium of claim 20, wherein thethird group of features includes a detection score for each of thedetected base candidates, a distance between a predicted position of abase in each of the detected LV candidates and each of the detected basecandidates, and an orientation distance between each of the detected LVcandidates and each of the detected base candidates.
 24. The computerreadable medium of claim 20, wherein the trained ranking model istrained based on geometrical relationships between LV candidates, apexcandidates, and base candidates detected in annotated training data.